磁带库系统的随机I/O调度算法

由于磁带库随机存取的性能很差,需要研究有效的随机I/O调度策略和算法以改善其在线存取的效率.对已有调度算法进行了分类、提炼和总结,利用仿真实验对静态调度、动态调度和基于复制的调度算法进行了深入研究,讨论了影响各种算法有效性的因素.针对已有算法在较重的负载条件下使系统性能急剧恶化的问题,还提出并研究了一种基于效益-代价均衡的调度算法.该算法引入效益-代价加权的概念,通过调节不同负载下的效益-代价加权比,极大地改善了已有算法在重负载下的有效性.该项研究为设计海量存储系统中的自适应调度算法提供了重要依据.     

基于Nginx的DRC集群动态负载均衡策略

面向数据的体系架构（DOA）为海量异构数据流通共享提供了新的有效解决方案。而数据注册中心（DRC）作为DOA的核心部件,它的访问性能尤为关键。针对高并发访问带来的DRC集群服务过载问题,采用Nginx反向代理负载均衡技术处理高并发访问。对Nginx的负载策略进行分析优化,提出一种由动态配置、负载收集、算法调度组成的动态负载均衡策略,并在负载调度模块对Nginx加权最小连接调度算法（WLC）进行改进,通过自适应权值不断调度下一个周期内性能最优的节点来处理请求。通过高并发性能测试验证了所提出的负载均衡策略在DRC集群中能更有效处理大流量的访问需求,提高集群的资源利用率和缩短请求响应时间。     

三级存储设备随机调度研究

随着信息时代的来临,世界上每时每刻都有大量的数据产生。因此有必要找到一种更加方便和有效的方式来管理这些海量的数据信息。为了解决海量信息管理中的数据存储问题,文章讨论了诸如磁带库、光盘库等三级存储设备在海量存储系统中的应用。另外,针对目前三级存储设备I/O调度算法的一些缺陷,提出了一种自适应的随机I/O调度算法来弥补这些缺点。     

非均质性海量复杂异构数据的混合云存储技术

在信息技术背景下,针对非均质性的海量复杂异构数据繁多、处理和存储困难等问题,提出了新型混合云存储系统架构.采用的方法是在该系统中设置数据管理中心、客户端、云端数据接口、网络数据融合算法模型、负载均衡器和多协议转换器,实现复杂数据的处理和计算,并采用网络数据融合算法实现非均质性的海量复杂异构数据的融合和计算,使系统具有较好的兼容性,提高了数据的存储能力.本研究还采用了加权最少连接调度算法,能够将接收到的数据请求分配到最合适的服务器中,提高了数据分配能力.     

基于OpenStack的Swift负载均衡算法

为了解决由于OpenStack的负载分发不均衡而引发的存储性能下降、资源利用率降低、I/O响应时长增加等问题,提出对加权最小连接调度算法进行改进. 通过对对象存储的负载均衡调度算法研究,利用存储节点的CPU、内存、硬盘、I/O资源利用率信息,并结合节点任务请求连接数,计算存储节点负载能力、性能和权值. 负载均衡器根据每个存储节点的权值大小判断任务分发方向. 经实验证明改进的负载均衡调度算法能够解决存储读写性能下降的问题,提升数据吞吐率、存储读写性能和系统稳定性.     

多跳中继无线网络资源复用的建模及算法设计

建立了中继网络资源复用问题的图论模型,依据该模型设计了自适应资源复用调度算法ARRS(adaptive resource reuse scheduling),以提高中继网络资源利用率.由于ARRS算法的核心步骤涉及顶加权图G(V,E,W)的染色,是NP-hard问题,为此给出了求解最优资源复用约束的顶加权图染色的近似算法ARRS_Greedy.该算法被证明具有时间复杂度O(|V|²),近似比为?(Δ+1)/2?(Δ表示图G顶点度数的最大值).该近似比是紧的.仿真分析验证了近似算法ARRS_Greedy在应用中取得了与最优解非常接近的性能,证明了ARRS算法能够动态适应网络状态变化,因而与现有算法相比大幅度提高了系统容量.     

关联性驱动的大数据处理任务调度方案

目前大数据处理过程较少关注任务所处理数据间的依赖关系,在任务执行过程中可能产生大量数据迁移,影响数据处理效率。为减少数据迁移,提升任务执行性能,从数据关联性及数据本地性两个角度出发,提出了一种数据关联性驱动的大数据处理任务优化调度方案：D³S²（Data-Dependency-Driven Scheduling Scheme）。D³S²由两部分组成：（1）数据关联性感知的数据优化放置机制（DAPM：Dependency-Aware Placement Mechanism）,根据日志信息挖掘数据关联性,进而将强关联的数据聚合并放置于相同机架上,减少了跨机架的数据迁移;（2）数据迁移代价感知的任务优化调度机制（TASM：Transfer-Aware Scheduling Mechanism）,完成数据放置后,以数据本地性为约束,对任务进行统一调度,最小化任务执行过程中的数据迁移代价。DAPM和TASM互相提供决策依据,以任务执行代价最小化为目标不断迭代调整调度方案,直至达到最优任务调度方案。之后在Hadoop平台上进行了验证,实验结果表明,较之原生Hadoop,在不增加作业完成时间的基础上,D³S²减少了作业执行过程中的数据迁移量。关键词：数据关联性;数据本地性;数据放置;任务调度;迁移代价感知中图法分类号：TP311     

一种OLAP海量数据载入技术的研究

在数据仓库、数据挖掘和联机分析处理系统中,海量数据的载入虽然不是时时发生,但是海量数据的载入效率直接影响着系统性能,如何高效地进行海量数据的载入十分重要.提出了两种技术,即基于UB-Tree的海量数据的初始化载入技术以及海量数据的增量载入技术,阐述了基于UB-Tree的海量数据载入的技术及其算法,提出了海量数据载入模型,建立基于UB-Tree的初始化载人,以及如何在已有的UB-Tree上做增量载入.经过性能分析,算法减少了I/O和CPU代价,为一种有效的海量数据载入方法.     

VOD服务器集群中的改进SLF存储调度策略

在VOD服务器集群中,存储调度策略是影响整个系统存储容量和总并发数的关键技术之一.针对现有存储调度策略中最小负载优先（SLF）副本放置算法调整代价过高的问题,提出了一种改进SLF算法.算法以最小化负载不平衡度和最小化副本调整代价为目标,在放置过程中充分利用当前已经存储的副本,降低副本调整的代价.仿真实验表明,基于改进SLF算法的存储调度策略可以最小化负载不平衡度,降低了存储调度的调整代价,同时提高了系统的用户请求接受概率.     

视频网格动态负载加权平滑均衡算法

为实现网格中视频资源服务的动态负载均衡,对一种动态负载加权均衡算法进行改进。利用监测与发现系统收集每台视频服务器的CPU利用率等主要负载参数,运用上述参数加权得到综合负载,对相邻时刻的负载做平滑处理以避免调度抖动。通过比较平滑后的动态负载值与服务器综合负载阈值进行动态调度,改变相应节点的负载,避免视频服务器间的负载失衡。实验结果表明,该算法能有效降低系统平均服务延迟时间并提高吞吐量,从而提升视频资源网格服务的整体性能。     

一种自适应负载的I/O调度算法

I/O调度算法对磁盘阵列(RAID)性能具有至关重要的影响。虽然已有很多典型的I/O调度算法在一定负载情况下可获得较好的性能,但很难有哪一种算法在各种负载情况下均能获得很好的性能。本文提出了一种智能RAID控制模型,结合C4.5决策树和AdaBoost算法实现负载自动分类,根据负载变化和性能反馈情况动态调整I/O调度策略,实现面向应用需求的自治调度。模拟实验结果表明,自适应调度算法具有较好的适应性,在各种负载情况下优于现有的I/O调度算法,尤其适用于多线程混合负载环境的I/O性能优化。     

一种面向混合实时事务调度的并发控制协议

首先给出了一个两层结构的混合实时数据库系统模型，其中支持采用非定期任务调度算法来改进系统的性能．进一步，针对这种模型下混合事务的数据一致性问题，提出了一种新的并发控制协议——MCC-DATI．该协议采用动态优先级驱动的调度算法，通过限制非定期的软实时事务对硬实时事务的阻塞时间，保证硬实时事务的可调度性；同时，采用非定期任务调度算法以及基于时间戳间隔的动态串行化顺序调整机制来减少软实时事务的截止期错失率．仿真实验表明，相对于先前的混合事务的并发控制协议，该协议在不同的系统负载与截止期约束下都能够改进系统的性能。     

两种经典实时调度算法的研究与实现

速率单调（RM）调度和最早截止期限优先（EDF）调度在实时调度领域占有重要低位。基于一个x86体系结构的小系统上设计实现RM和EDF调度算法，并在不同的工作负载下，以任务截止期错失率作为衡量不同任务调度算法性能优劣的指标，对两种算法进行了性能分析和比较。在通常情况下，RM和EDF都可以保证任务成功调度，EDF算法可承受较多的工作负载。但是随着负载的增加，EDF算法性能急剧下降，到一定过载程度，EDF算法性能低于RM算法。     

Online Bi-Objective Scheduling for IaaS Clouds Ensuring Quality of Service

This paper focuses on a bi-objective experimental evaluation of online scheduling in the Infrastructure as a Service model of Cloud computing regarding income and power consumption objectives. In this model, customers have the choice between different service levels. Each service level is associated with a price per unit of job execution time, and a slack factor that determines the maximal time span to deliver the requested amount of computing resources. The system, via the scheduling algorithms, is responsible to guarantee the corresponding quality of service for all accepted jobs. Since we do not consider any optimistic scheduling approach, a job cannot be accepted if its service guarantee will not be observed assuming that all accepted jobs receive the requested resources. In this article, we analyze several scheduling algorithms with different cloud configurations and workloads, considering the maximization of the provider income and minimization of the total power consumption of a schedule. We distinguish algorithms depending on the type and amount of information they require: knowledge free, energy-aware, and speed-aware. First, to provide effective guidance in choosing a good strategy, we present a joint analysis of two conflicting goals based on the degradation in performance. The study addresses the behavior of each strategy under each metric. We assess the performance of different scheduling algorithms by determining a set of non-dominated solutions that approximate the Pareto optimal set. We use a set coverage metric to compare the scheduling algorithms in terms of Pareto dominance. We claim that a rather simple scheduling approach can provide the best energy and income trade-offs. This scheduling algorithm performs well in different scenarios with a variety of workloads and cloud configurations.     

Simulation of power consumption of cloud data centers

Energy efficiency of cloud data centers received significant attention recently as data centers often consume significant resources in operation. Most of the existing energy-saving algorithms focus on resource consolidation for energy efficiency. This paper proposes a simulation-driven methodology with the accurate energy model to verify its performance, and introduces a new resource scheduling algorithm Best-Fit-Decreasing-Power (BFDP) to improve the energy efficiency without degrading the QoS of the system. Both the model and the resource algorithm have been extensively simulated and validated, and results showed that they are effective. In fact, the proposed model and algorithm outperforms the existing resource scheduling algorithms especially under light workloads.     

Improved Randomized Online Scheduling of Intervals and Jobs

We study the online preemptive scheduling of intervals and jobs (with restarts). Each interval or job has an arrival time, a deadline, a length and a weight. The objective is to maximize the total weight of completed intervals or jobs. While the deterministic case for intervals was settled a long time ago, the randomized case remains open. In this paper we first give a 2-competitive randomized algorithm for the case of equal length intervals. The algorithm is barely random in the sense that it randomly chooses between two deterministic algorithms at the beginning and then sticks with it thereafter. Then we extend the algorithm to cover several other cases of interval scheduling including monotone instances, C-benevolent instances and D-benevolent instances, giving the same competitive ratio. These algorithms are surprisingly simple but have the best competitive ratio against all previous (fully or barely) randomized algorithms. Next we extend the idea to give a 3-competitive algorithm for equal length jobs. Finally, we prove a lower bound of 2 on the competitive ratio of all barely random algorithms that choose between two deterministic algorithms for scheduling equal length intervals (and hence jobs). A preliminary version of this paper appeared in Fung et al. (The 6th International Workshop on Approximation and Online Algorithmspp, vol. 5426, pp. 53–66, 2008).     

Fundamentals of Scheduling and Performance of Video Tape Libraries

Robotic tape libraries are popular for applications with very high storage requirements, such as video servers. Here, we study the throughput of a tape library system, we design a new scheduling algorithm, the so-called Relief, and compare it against some older/straightforward ones, like FCFS, Maximum Queue Length (MQL) and an unfair one (Bypass), roughly equivalent to Shortest Job First. The proposed algorithm incorporates an aging mechanism in order to attain fairness and we prove that, under certain assumptions, it minimizes the average start-up latency. Extensive simulation experiments show that Relief outperforms its competitors (fair and unfair alike), with up to 203% improvement in throughput, for the same rejection ratio.     

A dynamic and reliability-driven scheduling algorithm for parallel real-time jobs executing on heterogeneous clusters

In this paper, a heuristic dynamic scheduling scheme for parallel real-time jobs executing on a heterogeneous cluster is presented. In our system model, parallel real-time jobs, which are modeled by directed acyclic graphs, arrive at a heterogeneous cluster following a Poisson process. A job is said to be feasible if all its tasks meet their respective deadlines. The scheduling algorithm proposed in this paper takes reliability measures into account, thereby enhancing the reliability of heterogeneous clusters without any additional hardware cost. To make scheduling results more realistic and precise, we incorporate scheduling and dispatching times into the proposed scheduling approach. An admission control mechanism is in place so that parallel real-time jobs whose deadlines cannot be guaranteed are rejected by the system. For experimental performance study, we have considered a real world application as well as synthetic workloads. Simulation results show that compared with existing scheduling algorithms in the literature, our scheduling algorithm reduces reliability cost by up to 71.4% (with an average of 63.7%) while improving schedulability over a spectrum of workload and system parameters. Furthermore, results suggest that shortening scheduling times leads to a higher guarantee ratio. Hence, if parallel scheduling algorithms are applied to shorten scheduling times, the performance of heterogeneous clusters will be further enhanced.     

Data‐aware task scheduling on heterogeneous hybrid memory multiprocessor systems

In this paper, we propose a method about task scheduling and data assignment on heterogeneous hybrid memory multiprocessor systems for real‐time applications. In a heterogeneous hybrid memory multiprocessor system, an important problem is how to schedule real‐time application tasks to processors and assign data to hybrid memories. The hybrid memory consists of dynamic random access memory and solid state drives when considering the performance of solid state drives into the scheduling policy. To solve this problem, we propose two heuristic algorithms called improvement greedy algorithm and the data assignment according to the task scheduling algorithm, which generate a near‐optimal solution for real‐time applications in polynomial time. We evaluate the performance of our algorithms by comparing them with a greedy algorithm, which is commonly used to solve heterogeneous task scheduling problem. Based on our extensive simulation study, we observe that our algorithms exhibit excellent performance and demonstrate that considering data allocation in task scheduling is significant for saving energy. We conduct experiments on two heterogeneous multiprocessor systems. Copyright © 2016 John Wiley & Sons, Ltd.